Detection of aberrantly spliced genes is an important step in RNA-seq-based rare-disease diagnostics. We recently developed FRASER, a denoising autoencoder-based method that outperformed alternative methods of detecting aberrant splicing. However, because FRASER’s three splice metrics are partially redundant and tend to be sensitive to sequencing depth, we introduce here a more robust intron-excision metric, the intron Jaccard index, that combines the alternative donor, alternative acceptor, and intron-retention signal into a single value. Moreover, we optimized model parameters and filter cutoffs by using candidate rare-splice-disrupting variants as independent evidence. On 16,213 GTEx samples, our improved algorithm, FRASER 2.0, called typically 10 times fewer splicing outliers while increasing the proportion of candidate rare-splice-disrupting variants by 10-fold and substantially decreasing the effect of sequencing depth on the number of reported outliers. To lower the multiple-testing correction burden, we introduce an option to select the genes to be tested for each sample instead of a transcriptome-wide approach. This option can be particularly useful when prior information, such as candidate variants or genes, is available. Application on 303 rare-disease samples confirmed the relative reduction in the number of outlier calls for a slight loss of sensitivity; FRASER 2.0 recovered 22 out of 26 previously identified pathogenic splicing cases with default cutoffs and 24 when multiple-testing correction was limited to OMIM genes containing rare variants. Altogether, these methodological improvements contribute to more effective RNA-seq-based rare diagnostics by drastically reducing the amount of splicing outlier calls per sample at minimal loss of sensitivity.

检测异常剪接基因是基于 RNA-seq 的罕见疾病诊断的重要一步。我们最近开发了 FRASER，一种基于去噪自动编码器的方法，其性能优于检测异常剪接的替代方法。然而，由于 FRASER 的三个剪接指标部分冗余，并且往往对测序深度敏感，因此我们在这里引入了一种更强大的内含子切除指标，即内含子杰卡德指数，它将替代供体、替代受体和内含子保留信号组合成单一值。此外，我们通过使用候选的罕见剪接破坏变体作为独立证据来优化模型参数和过滤截止值。在 16,213 个 GTEx 样本中，我们改进的算法 FRASER 2.0 通常将剪接异常值减少了 10 倍，同时将候选罕见剪接破坏变体的比例增加了 10 倍，并大幅降低了测序深度对报告的异常值数量的影响。为了降低多重测试校正负担，我们引入了一个选项来选择每个样本要测试的基因，而不是转录组范围的方法。当先验信息（例如候选变体或基因）可用时，此选项特别有用。对 303 个罕见疾病样本的应用证实，异常值数量相对减少，灵敏度略有下降；FRASER 2.0 在默认截止值的 26 个先前识别的致病性剪接案例中恢复了 22 个，当多重测试校正仅限于包含罕见变异的 OMIM 基因时，FRASER 2.0 恢复了 24 个。总而言之，这些方法上的改进在灵敏度损失最小的情况下大幅减少了每个样本的剪接异常值检出量，从而有助于更有效地进行基于 RNA-seq 的罕见诊断。